Comparative analysis of suicide attempt deaths and suicide survivors at one university hospital

Objective@#This study analyzed the characteristics of people who attempted suicide that resulted in deaths as compared to that of the suicide survivors. @*Methods@#This study included 799 suicide attempts that occurred from March 1, 2015, to March 31, 2019 at the emergency department of the university hospital in a city of around 300,000 people. Suicide attempts were classified into the survivor and death groups, and the characteristics of each group were compared. The suicide deaths due to re-attempts were also analyzed. @*Results@#There were more males than females in the death groups. There was a high proportion of people aged 50 or older in the death groups. Hanging, carbon monoxide poisoning, and jumping from great heights were the most commonly used methods of suicide in the death groups. In the selected death group, psychiatric symptom, physical illness, and economic problem among the suicidal causes and depressive disorder among the psychiatric diagnoses were factors that increase the risk of suicide death. Sixty-three point four percent of the survival groups and 52.5% of the selected deaths had not received psychiatric care. On the analysis of suicide deaths due to re-attempts, the average number of suicide attempts was 2.45±0.9. The time from the first suicide attempt to the last suicide attempt was 13.8±10.4 months. @*Conclusion@#If it is necessary to make a treatment decision for a suicide attempt in a limited time, such as the case of treating a suicide attempter who visits an emergency department, it is necessary to consider the characteristic factors of the death attempts of suicidal people.

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.